TC viewer

Setup

First, we need to import all the necessary libraries. Some of them are specifically developed to handle the download and plotting of the data and are hosted at the indicators set-up repository in GitHub

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import warnings
warnings.filterwarnings("ignore")
import sys
import xarray as xr
import requests
from io import BytesIO
import numpy as np

sys.path.append("../../../functions")
from tcs import Extract_Circle
from data_downloaders import download_ibtracs
# from plot_tcs_alba import Map

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_tcs import Map

lon_lat = [134.5, 5.5] #Palau location lon, lat
basin = 'WP'#'SP'
r1 = 5 # Radius of the circular area in degrees

IBTrACS

IBTrACS (International Best Track Archive for Climate Stewardship) is the most comprehensive global database of tropical cyclones. It compiles data from all major meteorological agencies worldwide, providing track, intensity, and metadata for storms from 1842 to present. It supports research on cyclone trends, risks, and climate variability.

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

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if update_data:
    url = 'https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r01/access/netcdf/IBTrACS.ALL.v04r01.nc'
    tcs = download_ibtracs(url, basin = basin)
    tcs.to_netcdf(f"{path_data}/tcs_{basin}.nc")
else:
    tcs = xr.load_dataset(f"{path_data}/tcs_{basin}.nc")

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d_vns = {
    'longitude': 'lon',
    'latitude': 'lat',
    'time': 'time',
    'pressure': 'wmo_pres',
    'wind': 'wmo_wind',
}
tcs_sel, tcs_sel_params = Extract_Circle(tcs, lon_lat[0], lon_lat[1], r1, d_vns)
tcs_sel['name'].values = [i.decode('utf-8') for i in tcs_sel['name'].values]
tcs_sel['year'] = tcs_sel.time.dt.year

tcs_1979 = tcs_sel.where(tcs_sel_params.dmin_date.dt.year >=1979, drop = True)

All TCs

This interactive plot allows you to zoom in and out to see all the TC tracks that have been developed in the vicinity of Palau. The vicinity of Palau in this case has been defined as a 5 degrees radius surrounding Koror.

mapHZ = Map()
fig = mapHZ.tcs_plotly(tcs_sel, lon_lat[0], lon_lat[1])
fig.update_layout(title=f"All TCs within {r1} degrees of {lon_lat[0]}E, {lon_lat[1]}N")
fig.update_layout(
    width=800,  # Ancho de la figura en píxeles
    height=600  # Altura de la figura en píxeles
)
fig.show()

From 1979

This interactive plot allows you to zoom in and out to see the TC tracks from 1979 that have been developed in the vicinity of Palau. The vicinity of Palau in this case has been defined as a 5 degrees radius surrounding Koror.

mapHZ = Map()
fig = mapHZ.tcs_plotly(tcs_1979, lon_lat[0], lon_lat[1])
fig.update_layout(title=f"All TCs within {r1} degrees of {lon_lat[0]}E, {lon_lat[1]}N")
fig.update_layout(
    width=800,  # Ancho de la figura en píxeles
    height=600  # Altura de la figura en píxeles
)
fig.show()

Severe TCs

Categories 3, 4 and 5

This interactive plot allows you to zoom in and out to see all the TC tracks that have been developed in the vicinity of Palau. The vicinity of Palau in this case has been defined as a 5 degrees radius surrounding Koror.

tcs_cat = tcs_sel.where(tcs_sel_params.category >= 3, drop = True)

mapHZ = Map()
fig = mapHZ.tcs_plotly(tcs_cat, lon_lat[0], lon_lat[1])
fig.update_layout(title=f"All TCs within {r1} degrees of {lon_lat[0]}E, {lon_lat[1]}N")
fig.update_layout(
    width=800,  # Ancho de la figura en píxeles
    height=600  # Altura de la figura en píxeles
)
fig.show()